We have explored different schedule sampling and frame skipping strategies with the RVOS model [1]} evaluated on the car class in the validation partition of the KITTI-MOTS benchmark [2]}. The task addressed is the one-shot (or semi-supervised) video object segmentation (VOS) task, where a mask of the object
is provided to the model to estimate the masks in the rest of the frames in the video sequence. All models are trained during a fixed amount of 40 epochs.
